Abstract
Artificial Intelligence can both empower individuals to face complex societal challenges and exacerbate problems and vulnerabilities, such as bias, inequalities, and polarization. For scientists, an open challenge is how to shape and regulate human-centered Artificial Intelligence ecosystems that help mitigate harms and foster beneficial outcomes oriented at the social good. In this tutorial, we discuss such an issue from two sides. First, we explore the network effects of Artificial Intelligence and their impact on society by investigating its role in social media, mobility, and economic scenarios. We further provide different strategies that can be used to model, characterize and mitigate the network effects of particular Artificial Intelligence driven individual behavior. Secondly, we promote the use of behavioral models as an addition to the data-based approach to get a further grip on emerging phenomena in society that depend on physical events for which no data are readily available. An example of this is tracking extremist behavior in order to prevent violent events. In the end, we illustrate some case studies in-depth and provide the appropriate tools to get familiar with these concepts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Afrin, T., Yodo, N.: A survey of road traffic congestion measures towards a sustainable and resilient transportation system. Sustainability 12(11), 4660 (2020)
Pariser, E.: The Filter Bubble: What the Internet is Hiding From You. Penguin UK, Westminster (2011)
Sunstein, C.R.: Republic. com. Princeton University Press, Princeton (2001)
Rycroft, R.S.: The Economics of Inequality, Discrimination, Poverty, and Mobility. Routledge, Milton Park (2017)
Schelling, T.C.: Models of segregation. Am. Econ. Rev. 59(2), 488–493 (1969)
Lorig, F., Vanhée, L., Dignum, F.: Agent-based social simulation for policy making (2022)
Erdős P., Rényi, A.: On random graphs. i. Publicationes Math. 6, 290–297 (1959)
Barabási, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)
Jean Tsang, S.: Cognitive discrepancy, dissonance, and selective exposure. Media Psychol. 22(3), 394–417 (2019)
Jeong, M., Zo, H., Lee, C.H., Ceran, Y.: Feeling displeasure from online social media postings: a study using cognitive dissonance theory. Comput. Hum. Behav. 97, 231–240 (2019)
Festinger, L.: A Theory of Cognitive Dissonance, vol. 2. Stanford University Press, Redwood City (1957)
Borah, P., Thorson, K., Hwang, H.: Causes and consequences of selective exposure among political blog readers: the role of hostile media perception in motivated media use and expressive participation. J. Inf. Technol. Polit. 12(2), 186–199 (2015)
Bozdag, E.: Bias in algorithmic filtering and personalization. Ethics Inf. Technol. 15(3), 209–227 (2013)
Ge, Y., et al.: Understanding echo chambers in e-commerce recommender systems. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2261–2270 (2020)
Braess, D.: Über ein paradoxon aus der verkehrsplanung. Unternehmensforschung 12, 258–268 (1968)
Lera, S.C., Pentland, A., Sornette, D.: Prediction and prevention of disproportionally dominant agents in complex networks. Proc. Natl. Acad. Sci. 117(44), 27090–27095 (2020)
Moore, M., Tambini, D.: Digital dominance: the power of Google. Facebook, and Apple. Oxford University Press, Amazon (2018)
Cook, P.J., Frank, R.H.: The winner-Take-all Society: Why the Few at the Top Get So Much More Than the Rest of Us. Random House, New York (2010)
Deffuant, G., Neau, D., Amblard, F., Weisbuch, G.: Mixing beliefs among interacting agents. Adv. Complex Syst. 3, 87–98 (2000)
Sîrbu, A., Pedreschi, D., Giannotti, F., Kertész, J.: Algorithmic bias amplifies opinion fragmentation and polarization: a bounded confidence model. PLoS ONE 14(3), e0213246 (2019)
Sun, S., Chen, J., Sun, J.: Congestion prediction based on GPS trajectory data. Int. J. Distrib. Sens. Netw. 15, 155014771984744 (2019)
Vaqar, S.A., Basir, O.: Traffic pattern detection in a partially deployed vehicular ad hoc network of vehicles. IEEE Wireless Commun. 16(6), 40–46 (2009)
Kruglanski, A.W., Gelfand, M.J., Bélanger, J.J., Sheveland, A., Hetiarachchi, M., Gunaratna, R.K.: The psychology of radicalization and deradicalization: How significance quest impacts violent extremism. Polit. Psychol. 35, 69–93 (2014)
Wei, Y., Singh, L., Martin, S.: Identification of extremism on Twitter. In: 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1251–1255. IEEE (2016)
Prabhu, A., et al.: Capitol (pat) riots: a comparative study of Twitter and parler. arXiv preprint arXiv:2101.06914 (2021)
van den Hurk, M., Dignum, F.: Towards fundamental models of radicalization. In: ESSA (2019)
Dignum, F., et al.: Analysing the combined health, social and economic impacts of the corovanvirus pandemic using agent-based social simulation. Minds Mach. 30(2), 177–194 (2020). https://doi.org/10.1007/s11023-020-09527-6
Pappalardo, L., Simini, F., Barlacchi, G., Pellungrini, R.: Scikit-mobility: a Python library for the analysis, generation and risk assessment of mobility data. arXiv preprint arXiv:1907.07062 (2019)
Rossetti, G., Milli, L., Rinzivillo, S., Sîrbu, A., Pedreschi, D., Giannotti, F.: Ndlib: a python library to model and analyze diffusion processes over complex networks. Int. J. Data Sci. Anal. 5(1), 61–79 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Pedreschi, D., Dignum, F., Morini, V., Pansanella, V., Cornacchia, G. (2023). Towards a Social Artificial Intelligence. In: Chetouani, M., Dignum, V., Lukowicz, P., Sierra, C. (eds) Human-Centered Artificial Intelligence. ACAI 2021. Lecture Notes in Computer Science(), vol 13500. Springer, Cham. https://doi.org/10.1007/978-3-031-24349-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-24349-3_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24348-6
Online ISBN: 978-3-031-24349-3
eBook Packages: Computer ScienceComputer Science (R0)